VLA/README.md

# Wall-X

<div align="left">

<!-- Links -->
<a href="https://huggingface.co/x-square-robot">
  <img src="https://img.shields.io/badge/Hugging%20Face-x--square--robot-FFB000?style=for-the-badge&logo=huggingface&logoColor=000" alt="Hugging Face">
</a>
<a href="https://x2robot.com/en/research/68bc2cde8497d7f238dde690">
  <img src="https://img.shields.io/badge/Project-1E90FF?style=for-the-badge&logo=google-chrome&logoColor=fff" alt="Project Page">
</a>

<!-- Tech stack -->
<br/>
<img src="https://img.shields.io/badge/Python-3.10-3776AB?style=flat&logo=python&logoColor=fff" alt="Python 3.10">
<img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=flat&logo=pytorch&logoColor=fff" alt="PyTorch">
<img src="https://img.shields.io/badge/FlashAttention-0F9D58?style=flat&logo=nvidia&logoColor=fff" alt="FlashAttention">
<img src="https://img.shields.io/badge/LeRobot-222?style=flat&logo=huggingface&logoColor=ffd21e" alt="LeRobot">
<img src="https://img.shields.io/badge/CUDA-12.x-76B900?style=flat&logo=nvidia&logoColor=fff" alt="CUDA">
<img src="https://img.shields.io/badge/OS-Ubuntu%2022.04-E95420?style=flat&logo=ubuntu&logoColor=fff" alt="Ubuntu 22.04">

</div>

## Building General-Purpose Robots Based on Embodied Foundation Model
We are building the embodied foundation model to capture and compress the world's most valuable data: the continuous, high-fidelity stream of physical interaction. 

By creating a direct feedback loop between the model's decisions and the body's lived experience, we enable the emergence of a truly generalizable intelligence—one that understands not just how the world works, but how to act effectively within it.

## Repository
This repository provides the training and inference code that supports our WALL series open-source embodied foundation models. It includes end-to-end pipelines for data preparation (LeRobot), model configuration, flow-matching and FAST action branches, and evaluation utilities for real and simulated robots.

## News
- We introduce [**WALL-OSS**](https://x2robot.com/en/research/68bc2cde8497d7f238dde690), an end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision–language understanding, (2) strong language–action association, and (3) robust manipulation capability.

## Models
- WALL-OSS-FLOW: https://huggingface.co/x-square-robot/wall-oss-flow
- WALL-OSS-FAST: https://huggingface.co/x-square-robot/wall-oss-fast

## Environment Setup

Create and activate conda environment:
```bash
conda create --name wallx python=3.10
conda activate wallx
```

Install requirements:
```bash
pip install -r requirements.txt
MAX_JOBS=4 pip install flash-attn==2.7.4.post1 --no-build-isolation
```

Install lerobot:
```bash
git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e .
```

Install wall_x:
```bash
git submodule update --init --recursive
MAX_JOBS=4 pip install --no-build-isolation --verbose .
```

## Training

### Finetune on LeRobot Datasets

Before training, please refer to `workspace/README.md` for detailed configuration instructions including:

Training script path configuration

- GPU setup
- Model and data paths
- Robot DOF configuration
- Training hyperparameters

Download the Flow/FAST pretrained model and run:
```bash
bash ./workspace/lerobot_example/run.sh
```

## Inference

For model inference, please refer to:

```bash
python ./scripts/fake_inference.py
```

This script demonstrates how to:
- Load the Wall-OSS model using `Qwen2_5_VLMoEForAction.from_pretrained()`
- Prepare input data including proprioceptive information, attention masks, and dataset specifications
- Run inference in validation mode with proper data types (bfloat16)
- Validate model outputs and check for numerical stability

To generate an open-loop comparison plot, please follow:

```bash
python ./scripts/draw_openloop_plot.py
```

## 📚 Cite Us

If you find WALL-OSS models useful, please cite:

```bibtex
@misc{walloss_paper_2025,
  title        = {WALL-OSS: Igniting VLMs toward the Embodied Space},
  author       = {X Square Robot},
  year         = {2025},
  howpublished = {\url{https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf}},
  note         = {White paper}
}
```